Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2931–2940 of 6661 papers

Title	Date	Tasks	Status	Hype
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where	Sep 22, 2023	Contrastive LearningSelf-Supervised Learning	CodeCode Available	0
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation	Sep 21, 2023	Contrastive LearningEEG	—Unverified	0
Audio Contrastive based Fine-tuning	Sep 21, 2023	Audio ClassificationContrastive Learning	—Unverified	0
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning	Sep 21, 2023	Contrastive LearningDiversity	—Unverified	0
TalkNCE: Improving Active Speaker Detection with Talk-Aware Contrastive Learning	Sep 21, 2023	Active Speaker DetectionAudio-Visual Active Speaker Detection	CodeCode Available	1
SCOB: Universal Text Understanding via Character-wise Supervised Contrastive Learning with Online Text Rendering for Bridging Domain Gap	Sep 21, 2023	Contrastive Learningdocument understanding	CodeCode Available	0
Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training	Sep 21, 2023	Contrastive LearningDecoder	CodeCode Available	0
A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network	Sep 21, 2023	Contrastive LearningData Augmentation	CodeCode Available	1
BitCoin: Bidirectional Tagging and Supervised Contrastive Learning based Joint Relational Triple Extraction Framework	Sep 21, 2023	Contrastive Learninggraph construction	—Unverified	0
BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision	Sep 21, 2023	Brain DecodingContrastive Learning	—Unverified	0

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All datasets imagenet-1k 10,000 People - Human Pose Recognition Data CIFAR-10 STL-10

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet50	ImageNet Top-1 Accuracy	73.6	—	Unverified
2	ResNet50	ImageNet Top-1 Accuracy	73	—	Unverified
3	ResNet50	ImageNet Top-1 Accuracy	71.1	—	Unverified
4	ResNet50	ImageNet Top-1 Accuracy	69.3	—	Unverified
5	ResNet50 (v2)	ImageNet Top-1 Accuracy	67.6	—	Unverified
6	ResNet50 (v2)	ImageNet Top-1 Accuracy	63.8	—	Unverified
7	ResNet50	ImageNet Top-1 Accuracy	63.6	—	Unverified
8	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
9	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
10	ResNet50 (4×)	ImageNet Top-1 Accuracy	61.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1	0..5sec	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	84.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	85.55	—	Unverified